Journal
REMOTE SENSING
Volume 12, Issue 17, Pages -Publisher
MDPI
DOI: 10.3390/rs12172823
Keywords
sugarcane; aboveground fresh weight; random forest regression; UAV-LiDAR; agricultural management
Categories
Funding
- National Key Research and Development Program of China [2017YFC1200100]
- Natural Science Foundation of China [41601181]
- Guangxi key research and development program [Gui Ke AB19245040]
- Open Research Fund of the Guangxi Key Laboratory ofWater Engineering Materials and Structures, Guangxi Hydraulic Research Institute [GXHRI-WEMS-2020-07]
- Scientific Research Program of Shanghai Science and Technology Commission [20dz1204702]
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Sugarcane is a multifunctional crop mainly used for sugar and renewable bioenergy production. Accurate and timely estimation of the sugarcane yield before harvest plays a particularly important role in the management of agroecosystems. The rapid development of remote sensing technologies, especially Light Detecting and Ranging (LiDAR), significantly enhances aboveground fresh weight (AFW) estimations. In our study, we evaluated the capability of LiDAR mounted on an Unmanned Aerial Vehicle (UAV) in estimating the sugarcane AFW in Fusui county, Chongzuo city of Guangxi province, China. We measured the height and the fresh weight of sugarcane plants in 105 sampling plots, and eight variables were extracted from the field-based measurements. Six regression algorithms were used to build the sugarcane AFW model: multiple linear regression (MLR), stepwise multiple regression (SMR), generalized linear model (GLM), generalized boosted model (GBM), kernel-based regularized least squares (KRLS), and random forest regression (RFR). The results demonstrate that RFR (R-2= 0.96, RMSE = 1.27 kg m(-2)) performs better than other models in terms of prediction accuracy. The final fitted sugarcane AFW distribution maps exhibited good agreement with the observed values (R-2= 0.97, RMSE = 1.33 kg m(-2)). Canopy cover, the distance to the road, and tillage methods all have an impact on sugarcane AFW. Our study provides guidance for calculating the optimum planting density, reducing the negative impact of human activities, and selecting suitable tillage methods in actual cultivation and production.
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